{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ARAR Model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ARAR model applies a memory-shortening transformation if the underlying process of a given time series ${Y_{t}, t = 1, 2, ..., n}$ is \"long-memory\" then it fits an autoregressive model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Memory Shortening\n",
    "\n",
    "The model follows five steps to classify ${Y_{t}}$ and take one of the following three actions:\n",
    "\n",
    "\n",
    "* L: declare ${Y_{t}}$ as long memory and form  ${Y_{t}}$ by ${\\tilde{Y}_{t} = Y_{t} - \\hat{\\phi}Y_{t - \\hat{\\tau}}}$\n",
    "* M: declare ${Y_{t}}$ as moderately long memory and form  ${Y_{t}}$ by ${\\tilde{Y}_{t} = Y_{t} - \\hat{\\phi}_{1}Y_{t -1} - \\hat{\\phi}_{2}Y_{t -2}}$\n",
    "* S: declare ${Y_{t}}$ as short memory.\n",
    "\n",
    "If ${Y_{t}}$ declared to be $L$ or $M$ then the series ${Y_{t}}$ is transformed again until. The transformation process continuous until the transformed series is classified as short memory. However, the maximum number of transformation process is three, it is very rare a time series require more than 2.\n",
    "\n",
    "* 1. For each $\\tau = 1, 2, ..., 15$, we find the value $\\hat{\\phi(\\tau)} $ of $\\hat{\\phi}$ that minimizes $ERR(\\phi, \\tau) = \\frac{\\sum_{t=\\tau +1 }^{n} [Y_{t} - \\phi Y_{t-\\tau}]^2 }{\\sum_{t=\\tau +1 }^{n} Y_{t}^{2}}$ then define $Err(\\tau) = ERR(\\hat{\\phi(\\tau), \\tau})$ and choose the lag $\\hat{\\tau}$ to be the value of $\\tau$ that minimizes  $Err(\\tau)$.\n",
    "* 2. If $Err(\\hat{\\tau}) \\leq  8/n$,  ${Y_{t}}$ is a long-memory series.\n",
    "* 3. If $\\hat{\\phi}( \\hat{\\tau} ) \\geq 0.93$ and $\\hat{\\tau} > 2$,  ${Y_{t}}$ is a long-memory series.\n",
    "* 4. If $\\hat{\\phi}( \\hat{\\tau} ) \\geq 0.93$ and $\\hat{\\tau} = 1$ or $2$, ${Y_{t}}$ is a long-memory series.\n",
    "* 5. If $\\hat{\\phi}( \\hat{\\tau} ) < 0.93$, ${Y_{t}}$ is a short-memory series.\n",
    "\n",
    "\n",
    "## Subset Autoregressive Model:\n",
    "\n",
    "In the following we will describe how ARAR algorithm fits an autoregressive process to the mean-corrected series $X_{t} = S_{t}- {\\bar{S}}$, $t = k+1, ..., n$ where ${S_{t}, t = k + 1, ..., n}$ is the memory-shortened version of  ${Y_{t}}$ which derived from the five steps we described above and $\\bar{S}$ is the sample mean of $S_{k+1}, ..., S_{n}$.\n",
    "\n",
    "The fitted model has the following form:\n",
    "\n",
    "$X_{t} = \\phi_{1}X{t-1} + \\phi_{1}X_{t-l_{1}} + \\phi_{1}X_{t- l_{1}} + \\phi_{1}X_{t-l_{1}} + Z$\n",
    "\n",
    "where $Z \\sim WN(0, \\sigma^{2})$. The coefficients $\\phi_{j}$ and white noise variance $\\sigma^2$ can be derived from the Yule-Walker equations for given lags $l_1, l_2,$ and $l_3$ :\n",
    "\n",
    "\\begin{equation}\n",
    "\\begin{bmatrix}\n",
    "1 & \\hat{\\rho}(l_1 - 1) & \\hat{\\rho}(l_2 - 1) & \\hat{\\rho}(l_3 - 1)\\\\\n",
    "\\hat{\\rho}(l_1 - 1) &1 & \\hat{\\rho}(l_2 - l_1) & \\hat{\\rho}(l_3 - l_1)\\\\\n",
    "\\hat{\\rho}(l_2 - 1) & \\hat{\\rho}(l_2 - l_1) & 1 & \\hat{\\rho}(l_2 - l_2)\\\\\n",
    "\\hat{\\rho}(l_3 - 1) & \\hat{\\rho}(l_3 - l_1) & \\hat{\\rho}(l_3 - l_1) & 1\n",
    "\\end{bmatrix}*\\begin{bmatrix}\n",
    "\\phi_{1} \\\\\n",
    "\\phi_{l_1} \\\\\n",
    "\\phi_{l_2}\\\\\n",
    "\\phi_{l_3}\n",
    "\\end{bmatrix} = \\begin{bmatrix} \\hat{\\rho}(1) \\\\ \\hat{\\rho}(l_1) \\\\ \\hat{\\rho}(l_2)\\\\ \\hat{\\rho}(l_3) \\end{bmatrix}\n",
    "\\end{equation}\n",
    "\n",
    "and $\\sigma^2 = \\hat{\\gamma}(0) [1-\\phi_1\\hat{\\rho}(1)] - \\phi_{l_1}\\hat{\\rho}(l_1)] - \\phi_{l_2}\\hat{\\rho}(l_2)] - \\phi_{l_3}\\hat{\\rho}(l_3)]$, where $\\hat{\\gamma}(j)$ and $\\hat{\\rho}(j), j = 0, 1, 2, ...,$ are the sample autocovariances and autocorelations of the series $X_{t}$.\n",
    "\n",
    "The algorithm computes the coefficients of $\\phi(j)$ for each set of lags where $1<l_1<l_2<l_3 \\leq m$ where m chosen to be 13 or 26. The algorithm selects the model that the Yule-Walker estimate of $\\sigma^2$ is minimal.\n",
    "\n",
    "## Forecasting\n",
    "\n",
    "If short-memory filter found in first step it has coefficients $\\Psi_0, \\Psi_1, ..., \\Psi_k (k \\geq0)$ where $\\Psi_0 = 1$. In this case the transforemed series can be expressed as\n",
    "\\begin{equation}\n",
    "    S_t = \\Psi(B)Y_t = Y_t + \\Psi_1 Y_{t-1} + ...+ \\Psi_k Y_{t-k},\n",
    "\\end{equation}\n",
    "where $\\Psi(B) = 1 + \\Psi_1B + ...+ \\Psi_k B^k$ is polynomial in the back-shift operator.\n",
    "\n",
    "If the coefficients of the subset autoregression found in the second step it has coefficients $\\phi_1, \\phi_{l_1},  \\phi_{l_2}$ and $\\phi_{l_3}$ then the subset AR model for $X_t = S_t - \\bar{S}$ is\n",
    "\n",
    "\\begin{equation}\n",
    "    \\phi(B)X_t = Z_t,\n",
    "\\end{equation}\n",
    "\n",
    "where $Z_t$ is a white-noise series with zero mean and constant variance and $\\phi(B) = 1 - \\phi_1B - \\phi_{l_1}B^{l_1} - \\phi_{l_2}B^{l_2} - \\phi_{l_3}B^{l_3}$. From equation (1) and (2) one can obtain\n",
    "\n",
    "\\begin{equation}\n",
    "    \\xi(B)Y_t = \\phi(1)\\bar{S} + Z_t,\n",
    "\\end{equation}\n",
    "where $\\xi (B) = \\Psi(B)\\phi(B)$.\n",
    "\n",
    "Assuming the fitted model in equation (3) is an appropriate model, and $Z_t$ is uncorrelated with $Y_j, j <t$ $\\forall t \\in T$, one can determine minimum mean squared error linear predictors $P_n Y_{n + h}$ of $Y_{n+h}$ in terms of ${1, Y_1, ..., Y_n}$ for $n > k + l_3$, from recursions\n",
    "\n",
    "\\begin{equation}\n",
    "    P_n Y_{n+h} = - \\sum_{j = 1}^{k + l_3} \\xi P_nY_{n+h-j} + \\phi(1)\\bar{S},  h\\geq 1,\n",
    "\\end{equation}\n",
    "with the initial conditions $P_n Y_{n+h} = Y_{n + h}$, for $h\\leq0$.\n",
    "\n",
    "Ref: Brockwell, Peter J, and Richard A. Davis. Introduction to Time Series and Forecasting. [Springer](https://link.springer.com/book/10.1007/978-3-319-29854-2) (2016)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div role=\"note\"\n",
    "     style=\"background: rgba(16,142,233,0.1); border-left: 4px solid #1890ff;\n",
    "            border-radius: 4px; padding: 10px 14px; margin: 1em 0;\">\n",
    "  <p style=\"display:flex; align-items:center; font-size:1rem; color:#1890ff;\n",
    "            margin:0 0 6px 0; font-weight:500;\">\n",
    "    <span style=\"margin-right:6px; font-size:18px;\">ℹ️</span> Note\n",
    "  </p>\n",
    "\n",
    "  <p style=\"margin:0; color:inherit;\">\n",
    "  \n",
    "  The python implementation of the ARAR algorithm in skforecast is based on the Julia package <a href=\"https://taf-society.github.io/Durbyn.jl/dev/\">Durbyn.jl</a> develop by Resul Akay.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Libraries and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries\n",
    "# ==============================================================================\n",
    "import matplotlib.pyplot as plt\n",
    "from skforecast.stats import Arar\n",
    "from skforecast.recursive import ForecasterStats\n",
    "from skforecast.model_selection import TimeSeriesFold, backtesting_stats\n",
    "from skforecast.datasets import fetch_dataset\n",
    "from skforecast.plot import set_dark_theme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭──────────────────────────────── <span style=\"font-weight: bold\">fuel_consumption</span> ────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                     │\n",
       "│ Monthly fuel consumption in Spain from 1969-01-01 to 2022-08-01.                 │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                          │\n",
       "│ Obtained from Corporación de Reservas Estratégicas de Productos Petrolíferos and │\n",
       "│ Corporación de Derecho Público tutelada por el Ministerio para la Transición     │\n",
       "│ Ecológica y el Reto Demográfico. https://www.cores.es/es/estadisticas            │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/consumos-combustibles-mensual.csv                             │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Shape:</span> 644 rows x 5 columns                                                      │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭──────────────────────────────── \u001b[1mfuel_consumption\u001b[0m ────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                     │\n",
       "│ Monthly fuel consumption in Spain from 1969-01-01 to 2022-08-01.                 │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                          │\n",
       "│ Obtained from Corporación de Reservas Estratégicas de Productos Petrolíferos and │\n",
       "│ Corporación de Derecho Público tutelada por el Ministerio para la Transición     │\n",
       "│ Ecológica y el Reto Demográfico. https://www.cores.es/es/estadisticas            │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/consumos-combustibles-mensual.csv                             │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mShape:\u001b[0m 644 rows x 5 columns                                                      │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "date\n",
       "1969-01-01    166875.2129\n",
       "1969-02-01    155466.8105\n",
       "1969-03-01    184983.6699\n",
       "1969-04-01    202319.8164\n",
       "1969-05-01    206259.1523\n",
       "                 ...     \n",
       "1989-09-01    687649.2852\n",
       "1989-10-01    669889.1602\n",
       "1989-11-01    601413.8867\n",
       "1989-12-01    663568.1055\n",
       "1990-01-01    610241.2461\n",
       "Freq: MS, Name: y, Length: 253, dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Download data\n",
    "# ==============================================================================\n",
    "data = fetch_dataset(name='fuel_consumption', raw=False)\n",
    "data = data.loc[:'1990-01-01 00:00:00']\n",
    "y = data['Gasolinas'].rename('y').rename_axis('date')\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ARAR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Skforecast** provides the class [`ARAR`](https://skforecast.org/latest/api/stats#arar) to facilitate the implementation of ARAR models in Python, allowing users to easily fit and forecast time series data using this approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
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       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
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       "  color: var(--sklearn-color-text);\n",
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       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
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       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
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       "\n",
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       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
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       "\n",
       "#sk-container-id-1 div.sk-container {\n",
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       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
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       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
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       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
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       "  background-position: center center;\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
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       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
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       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
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       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Arar(max_ar_depth=26, max_lag=40)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Arar</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>Arar(max_ar_depth=26, max_lag=40)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "Arar(max_ar_depth=26, max_lag=40)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ARAR model\n",
    "# ==============================================================================\n",
    "model = Arar()\n",
    "model.fit(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once de model is fitted, future observations can be forecasted using the `predict` and `predict_interval` methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([576270.29065713, 711350.90294941, 645064.14251878, 699974.70526107,\n",
       "       693641.4876215 , 813391.3131971 , 849840.34223407, 728834.11322404,\n",
       "       698899.25161967, 640834.1450568 ])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prediction\n",
    "# ==============================================================================\n",
    "model.predict(steps=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>lower_95</th>\n",
       "      <th>upper_95</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>step</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>576270.290657</td>\n",
       "      <td>535285.283204</td>\n",
       "      <td>617255.298110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>711350.902949</td>\n",
       "      <td>669971.979006</td>\n",
       "      <td>752729.826893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>645064.142519</td>\n",
       "      <td>597182.272797</td>\n",
       "      <td>692946.012240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>699974.705261</td>\n",
       "      <td>651641.922678</td>\n",
       "      <td>748307.487844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>693641.487621</td>\n",
       "      <td>643148.019307</td>\n",
       "      <td>744134.955936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>813391.313197</td>\n",
       "      <td>762568.607694</td>\n",
       "      <td>864214.018700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>849840.342234</td>\n",
       "      <td>798207.532180</td>\n",
       "      <td>901473.152288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>728834.113224</td>\n",
       "      <td>677001.238600</td>\n",
       "      <td>780666.987848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>698899.251620</td>\n",
       "      <td>646741.565713</td>\n",
       "      <td>751056.937527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>640834.145057</td>\n",
       "      <td>588566.126391</td>\n",
       "      <td>693102.163723</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               mean       lower_95       upper_95\n",
       "step                                             \n",
       "1     576270.290657  535285.283204  617255.298110\n",
       "2     711350.902949  669971.979006  752729.826893\n",
       "3     645064.142519  597182.272797  692946.012240\n",
       "4     699974.705261  651641.922678  748307.487844\n",
       "5     693641.487621  643148.019307  744134.955936\n",
       "6     813391.313197  762568.607694  864214.018700\n",
       "7     849840.342234  798207.532180  901473.152288\n",
       "8     728834.113224  677001.238600  780666.987848\n",
       "9     698899.251620  646741.565713  751056.937527\n",
       "10    640834.145057  588566.126391  693102.163723"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prediction interval\n",
    "# ==============================================================================\n",
    "model.predict_interval(steps=10, level=[95])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ForecasterStats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The previous section introduced the construction of ARAR models. In order to seamlessly integrate these models with the various functionalities provided by **skforecast**, the next step is to encapsulate the skforecast [`ARAR`](https://skforecast.org/latest/api/stats#arar) model within a [`ForecasterStats`](https://skforecast.org/latest/api/forecasterstats) object. This encapsulation harmonizes the intricacies of the model and allows for the coherent use of skforecast's extensive capabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
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       "            font-size: 0.9em;\n",
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       "            border: 1px solid #ddd;\n",
       "            background-color: #f0f8ff;\n",
       "            padding: 5px 15px;\n",
       "            border-radius: 8px;\n",
       "            max-width: 600px;\n",
       "            #margin: auto;\n",
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       "        .container-2acb6e48b1594569979640c95c0796ab h2 {\n",
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       "            color: #222222;\n",
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       "            margin-bottom: 15px;\n",
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       "            content: \"- \";\n",
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       "        .container-2acb6e48b1594569979640c95c0796ab a {\n",
       "            color: #001633;\n",
       "            text-decoration: none;\n",
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       "        .container-2acb6e48b1594569979640c95c0796ab a:hover {\n",
       "            color: #359ccb; \n",
       "        }\n",
       "    </style>\n",
       "    \n",
       "        <div class=\"container-2acb6e48b1594569979640c95c0796ab\">\n",
       "            <p style=\"font-size: 1.5em; font-weight: bold; margin-block-start: 0.83em; margin-block-end: 0.83em;\">ForecasterStats</p>\n",
       "            <details open>\n",
       "                <summary>General Information</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Estimator:</strong> Arar</li>\n",
       "                    <li><strong>Window size:</strong> 1</li>\n",
       "                    <li><strong>Series name:</strong> y</li>\n",
       "                    <li><strong>Exogenous included:</strong> False</li>\n",
       "                    <li><strong>Creation date:</strong> 2025-11-26 15:05:12</li>\n",
       "                    <li><strong>Last fit date:</strong> 2025-11-26 15:05:12</li>\n",
       "                    <li><strong>Skforecast version:</strong> 0.19.0</li>\n",
       "                    <li><strong>Python version:</strong> 3.12.11</li>\n",
       "                    <li><strong>Forecaster id:</strong> None</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Exogenous Variables</summary>\n",
       "                <ul>\n",
       "                    None\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Data Transformations</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Transformer for y:</strong> None</li>\n",
       "                    <li><strong>Transformer for exog:</strong> None</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Training Information</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Training range:</strong> [Timestamp('1969-01-01 00:00:00'), Timestamp('1990-01-01 00:00:00')]</li>\n",
       "                    <li><strong>Training index type:</strong> DatetimeIndex</li>\n",
       "                    <li><strong>Training index frequency:</strong> MS</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Estimator Parameters</summary>\n",
       "                <ul>\n",
       "                    {'max_ar_depth': 26, 'max_lag': 40, 'safe': True}\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Fit Kwargs</summary>\n",
       "                <ul>\n",
       "                    {}\n",
       "                </ul>\n",
       "            </details>\n",
       "            <p>\n",
       "                <a href=\"https://skforecast.org/0.19.0/api/forecasterstats.html\">&#128712 <strong>API Reference</strong></a>\n",
       "                &nbsp;&nbsp;\n",
       "                <a href=\"https://skforecast.org/0.19.0/user_guides/forecasting-sarimax-arima.html\">&#128462 <strong>User Guide</strong></a>\n",
       "            </p>\n",
       "        </div>\n",
       "        "
      ],
      "text/plain": [
       "=============== \n",
       "ForecasterStats \n",
       "=============== \n",
       "Estimator: Arar(max_ar_depth=26, max_lag=40) \n",
       "Series name: y \n",
       "Exogenous included: False \n",
       "Exogenous names: None \n",
       "Transformer for y: None \n",
       "Transformer for exog: None \n",
       "Training range: [Timestamp('1969-01-01 00:00:00'), Timestamp('1990-01-01 00:00:00')] \n",
       "Training index type: DatetimeIndex \n",
       "Training index frequency: MS \n",
       "Estimator parameters: {'max_ar_depth': 26, 'max_lag': 40, 'safe': True} \n",
       "fit_kwargs: {} \n",
       "Creation date: 2025-11-26 15:05:12 \n",
       "Last fit date: 2025-11-26 15:05:12 \n",
       "Index seen by the forecaster: DatetimeIndex(['1969-01-01', '1969-02-01', '1969-03-01', '1969-04-01',\n",
       "               '1969-05-01', '1969-06-01', '1969-07-01', '1969-08-01',\n",
       "               '1969-09-01', '1969-10-01',\n",
       "               ...\n",
       "               '1989-04-01', '1989-05-01', '1989-06-01', '1989-07-01',\n",
       "               '1989-08-01', '1989-09-01', '1989-10-01', '1989-11-01',\n",
       "               '1989-12-01', '1990-01-01'],\n",
       "              dtype='datetime64[ns]', name='date', length=253, freq='MS') \n",
       "Skforecast version: 0.19.0 \n",
       "Python version: 3.12.11 \n",
       "Forecaster id: None "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create and fit ForecasterStats\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterStats(estimator=Arar())\n",
    "forecaster.fit(y=y)\n",
    "forecaster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lag_2</td>\n",
       "      <td>0.568527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lag_14</td>\n",
       "      <td>0.318155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lag_1</td>\n",
       "      <td>0.138978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>lag_12</td>\n",
       "      <td>-0.351038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  feature  importance\n",
       "0   lag_2    0.568527\n",
       "1  lag_14    0.318155\n",
       "2   lag_1    0.138978\n",
       "3  lag_12   -0.351038"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature importances\n",
    "# ==============================================================================\n",
    "forecaster.get_feature_importances()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1990-02-01    576270.290657\n",
       "1990-03-01    711350.902949\n",
       "1990-04-01    645064.142519\n",
       "Freq: MS, Name: pred, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict\n",
    "# ==============================================================================\n",
    "predictions = forecaster.predict(steps=10)\n",
    "predictions.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pred</th>\n",
       "      <th>lower_bound</th>\n",
       "      <th>upper_bound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-02-01</th>\n",
       "      <td>576270.290657</td>\n",
       "      <td>535285.283204</td>\n",
       "      <td>617255.298110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-03-01</th>\n",
       "      <td>711350.902949</td>\n",
       "      <td>669971.979006</td>\n",
       "      <td>752729.826893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-04-01</th>\n",
       "      <td>645064.142519</td>\n",
       "      <td>597182.272797</td>\n",
       "      <td>692946.012240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     pred    lower_bound    upper_bound\n",
       "1990-02-01  576270.290657  535285.283204  617255.298110\n",
       "1990-03-01  711350.902949  669971.979006  752729.826893\n",
       "1990-04-01  645064.142519  597182.272797  692946.012240"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict intervals\n",
    "# ==============================================================================\n",
    "predictions = forecaster.predict_interval(steps=36, alpha=0.05)\n",
    "predictions.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Backtesting\n",
    "\n",
    "ARAR and other statistical models, once integrated in a [`ForecasterStats`](https://skforecast.org/latest/api/forecasterstats) object, can be evaluated using any of the [backtesting strategies](../introduction-forecasting/introduction-forecasting.html#backtesting-forecasting-models) implemented in skforecast."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fe296189023c4e11b5d4a8df3a482740",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/9 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Backtesting\n",
    "# ==============================================================================\n",
    "cv = TimeSeriesFold(\n",
    "    initial_train_size = 150,\n",
    "    steps              = 12,\n",
    "    refit              = True,\n",
    ")\n",
    "\n",
    "metric, predictions = backtesting_stats(\n",
    "    y               = y,\n",
    "    forecaster      = forecaster,\n",
    "    cv              = cv,\n",
    "    interval        = [2.5, 97.5],\n",
    "    metric          = 'mean_absolute_error',\n",
    "    verbose         = False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fold</th>\n",
       "      <th>pred</th>\n",
       "      <th>lower_bound</th>\n",
       "      <th>upper_bound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1981-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>585006.456464</td>\n",
       "      <td>548872.543529</td>\n",
       "      <td>621140.369400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>632872.256680</td>\n",
       "      <td>596247.977571</td>\n",
       "      <td>669496.535788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-09-01</th>\n",
       "      <td>0</td>\n",
       "      <td>515431.057548</td>\n",
       "      <td>474418.134356</td>\n",
       "      <td>556443.980739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-10-01</th>\n",
       "      <td>0</td>\n",
       "      <td>523423.286271</td>\n",
       "      <td>481982.529292</td>\n",
       "      <td>564864.043250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            fold           pred    lower_bound    upper_bound\n",
       "1981-07-01     0  585006.456464  548872.543529  621140.369400\n",
       "1981-08-01     0  632872.256680  596247.977571  669496.535788\n",
       "1981-09-01     0  515431.057548  474418.134356  556443.980739\n",
       "1981-10-01     0  523423.286271  481982.529292  564864.043250"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Backtest predictions\n",
    "# ==============================================================================\n",
    "predictions.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predictions\n",
    "# ==============================================================================\n",
    "set_dark_theme()\n",
    "fig, ax = plt.subplots(figsize=(7, 4))\n",
    "y.loc[predictions.index].plot(ax=ax, label='y')\n",
    "predictions['pred'].plot(ax=ax, label='predictions')\n",
    "ax.fill_between(\n",
    "        predictions.index,\n",
    "        predictions['lower_bound'],\n",
    "        predictions['upper_bound'],\n",
    "        label='prediction interval',\n",
    "        color='gray',\n",
    "        alpha=0.6,\n",
    "        zorder=1\n",
    "    )\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "skforecast_py12",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.11"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
